eoguzakin/Robustness-of-AI-Generated-Image-Detection-Against-Localized-Inpainting-Attacks

Robustness of AI-Generated Image Detection Against Localized Inpainting Attacks

This repository hosts the detector-ready datasets and mask packs used in the thesis:

Robustness of AI-Generated Image Detection Against Localized Inpainting Attacks
Oguz Akin, Saarland University, CISPA Helmholtz Center for Information Security (2025)

It provides standardized evaluation splits for six state-of-the-art AI-generated image (AIGI) detectors across watermarking, passive, and training-free paradigms, tested under LaMa and ZITS inpainting attacks.

Everything is packaged as .tar.xz archives to ensure reproducibility and easy transfer.

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📂 Repository Structure

. ├─ detectors/ │ ├─ ufd_datasets.tar.xz │ ├─ dimd_datasets.tar.xz │ ├─ warpad_datasets.tar.xz │ ├─ aeroblade_datasets.tar.xz │ ├─ stablesig_datasets.tar.xz │ └─ treering_datasets.tar.xz ├─ masks/ │ ├─ masks_stablesig.tar.xz │ ├─ robust_randblob_bins.tar.xz │ ├─ robust_randrect.tar.xz │ └─ masks_treering_wm.tar.xz └─ checksums.sha256

• detectors/ — per-detector dataset “views,” already resized/re-encoded into the formats expected by each model.
• masks/ — random-rectangle and random-blob object masks (area-binned), used to generate inpainting attacks.
• checksums.sha256 — SHA-256 integrity hashes for all archives.

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🔎 Dataset Details

Detector Views

Each archive expands into the exact layout expected by that detector. All splits contain 200 images per split (e.g. LaMa Inpainted Rand-Blob on SEMI-TRUTHS real images).

Typical layout: baseline/ reals/ fakes/ [fakes_inpainted_lama/, fakes_inpainted_zits/]

robustness/ inpainted_lama/ randrect/ randblob_bins/bin{1..4}/ inpainted_zits/ randrect/ randblob_bins/bin{1..4}/ [reals_inpainted/]

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Detector Input Handling

On disk: All datasets are stored with their preprocessed versions for each detector to match their original paper/training setup.

• UFD → 224
  (Resized + center-cropped to 224×224, CLIP normalization.)
• DIMD → JPEG-256
  (Resized to 256×256, with JPEG round-trip to mimic training distribution.)
• AEROBLADE / WaRPAD / StableSig / Tree-Ring → 512
  (All evaluated directly at 512×512 without JPEG compression.)

Why this split? To eliminate the effect of compression or size on classification, ensuring scientifically fair evaluation.

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Mask Packs

• masks_stablesig.tar.xz
• masks_treering_wm.tar.xz

Contain random rectangle and random blob masks, binned by area ratio:

• bin1_0-3 → 0–3% of image area
• bin2_3-10 → 3–10%
• bin3_10-25 → 10–25%
• bin4_25-40 → 25–40%

Used with LaMa and ZITS to create controlled inpainting attacks.

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📐 Metrics

Datasets are organized to support a fixed-threshold robustness evaluation.

• Baseline AUC
  Distinguish clean reals vs fakes. Threshold t* chosen via Youden’s J.
• Robustness AUC
  Distinguish clean vs inpainted on correctly classified baseline samples.
• ΔAUC = Baseline – Robustness
• ASR_inpainted (primary):
  % of inpainted reals classified as Real at baseline t*.
• ASR_fake→real (secondary):
  % of baseline-detected fakes that flip to Real after inpainting.

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📦 Archive Sizes

• detectors/aeroblade_datasets.tar.xz — 1.5 GB
• detectors/dimd_datasets.tar.xz — 117 MB
• detectors/warpad_datasets.tar.xz — 1.5 GB
• detectors/stablesig_datasets.tar.xz — 924 MB
• detectors/treering_datasets.tar.xz — 1.6 GB
• detectors/ufd_datasets.tar.xz — 442 MB
• masks/masks_stablesig.tar.xz — 2.2 MB
• masks/masks_treering_wm.tar.xz — 1.2 MB

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⚙️ Usage

Download & Extract

from huggingface_hub import hf_hub_download
import tarfile, os

REPO = "eoguzakin/Robustness of AI-Generated Image Detection Against Localized Inpainting Attacks"

def fetch_and_extract(filename, target_dir):
    path = hf_hub_download(repo_id=REPO, filename=filename, repo_type="dataset")
    os.makedirs(target_dir, exist_ok=True)
    with tarfile.open(path, "r:xz") as tar:
        tar.extractall(target_dir)
    print("Extracted:", target_dir)

# Example: UFD view + StableSig masks
fetch_and_extract("detectors/ufd_datasets.tar.xz", "/tmp/ufd")
fetch_and_extract("masks/masks_stablesig.tar.xz", "/tmp/masks_stablesig")

Integrity check

sha256sum -c checksums.sha256

🧪 Provenance Reals: SEMI-TRUTHS (Pal et al. 2024), OpenImages subset.

Fakes: GenImage diverse generator set.

Inpainting attacks: LaMa (Suvorov et al. 2022), ZITS (Dong et al. 2022).

Watermarks: Stable Signature (Fernandez et al. 2023), Tree-Ring (Wen et al. 2023).

Detector-specific preprocessing applied before runtime, ensuring comparability.

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📸 Sample Images

Baseline (Real vs Fake)

Real	Fake

Inpainted reals (LaMa, ZITS & SEMI-TRUTHS)

LaMa	ZITS	SEMI-TRUTHS

LaMa	ZITS

Watermarks (StableSig vs Tree-Ring)

StableSig	LaMa	ZITS

Tree-Ring	LaMa	ZITS

Generated sample mask images. Upper and middle rows are composed of masks with sizes 0-3%, 3-10%, 10-25% and 25-40% from left to right. Last row is composed of random samples of geometric masks with different sizes.

An example semantic mask used in Semi-Truths dataset.

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📚 Citations If you use this dataset, please cite:

• Pal et al., 2024 — Semi-Truths: A Large-Scale Dataset of AI-Augmented Images for Evaluating Robustness of AI-Generated Image Detectors.
• Zhu et al., 2023 — GenImage: A Million-Scale Benchmark for Detecting AI-Generated Image.
• Ojha et al., 2023 — Universal Fake Image Detectors.
• Corvi et al., 2023 — On the Detection of Synthetic Images Generated by Diffusion Models.
• Choi et al., 2025 — Training-free Detection of AI-generated images via Cropping Robustness (WaRPAD)
• Ricker et al., 2024 — AEROBLADE.
• Fernandez et al., 2023 — Stable Signature.
• Wen et al., 2023 — Tree-Ring Watermarks.
• Suvorov et al., 2022 — LaMa Inpainting.
• Dong et al., 2022 — ZITS Inpainting.

📝 License Derived datasets for research use only. Upstream datasets (SEMI-TRUTHS, GenImage, LaMa, ZITS, etc.) retain their original licenses. This packaging (scripts + archive structure) is released under CC BY-NC 4.0 unless otherwise specified.

👤 Maintainer Oguz Akin — Saarland University Contact: [email protected]